The Triage Problem Nobody Solved

The average enterprise security stack now counts between forty-five and seventy-five tools. An analyst investigating a single suspicious login touches six of them: the SIEM for the alert, the identity provider for auth history, the EDR console for host state, the cloud console for API activity, the ticketing system for context, and a threat-intel portal for reputation. Each hop costs minutes, a login, and a mental context switch — and each tool speaks its own schema, so the analyst becomes a human ETL pipeline, joining datasets in their head at 2 a.m.

Vendors sell integration as the answer, but a webhook that copies an alert from one queue to another is not integration; it is duplication with extra latency. The join the analyst actually needs — this identity, across these five systems, over the last ninety days, compared against its own baseline — does not exist in any single console. It has to be rebuilt by hand for every investigation, which is why an investigation that should take ten minutes takes four hours.

The strategic cost is worse than the operational one. Because context lives in fragments, every conclusion is provisional. Analysts hedge, escalate, and re-investigate, and CISOs discover during incidents that their seventy tools produce seventy partial stories and no authoritative one.

Every enterprise security team now serves two masters: the attackers and the auditors. SOC 2, ISO 27001, PCI DSS, DORA, HIPAA — each framework wants proof that monitoring exists, that alerts are investigated, that incidents follow procedure, that access reviews happen. Assembling that proof is a quarterly scramble of screenshots, CSV exports, and reconstructed timelines, performed by the same senior people who are supposed to be hunting threats.

The dirty secret is that most of this evidence is theatre. A screenshot of a dashboard proves the dashboard existed on the day of the screenshot. It says nothing about whether the alert at 3 a.m. on a Sunday in February was actually worked, by whom, and on what basis. Auditors accept it because nothing better is usually available, and everyone in the room quietly knows it.

Regulators are tightening. DORA and the SEC disclosure rules ask for operational reality, not intent — how fast incidents were detected, classified, reported. That standard cannot be met retroactively with screenshots. It requires an operating model where every detection, verdict, and response action generates its own tamper-evident record as a by-product of doing the work, not as a separate documentation exercise.

The modern enterprise attack surface is not a network edge; it is a directory. Tens of thousands of human identities, and — increasingly — multiples more non-human ones: service accounts, API keys, CI runners, and now AI agents with standing credentials. Attackers noticed years ago that stealing a valid credential beats exploiting a vulnerability: no malware, no exploit signature, just a login that looks almost right. The majority of serious cloud incidents now begin with a compromised identity, not a compromised host.

Almost right is the operative phrase. A stolen credential logs in from a slightly wrong place, at a slightly wrong hour, and touches resources slightly outside its habit. Each signal alone is dismissible — which is exactly why rule-based detection drowns: the rules fire on travellers and contractors all day, and the SOC tunes them down until the real one sails through.

Catching identity abuse requires knowing each identity's normal — its hours, geographies, volumes, and peer group — and evaluating every anomaly against that baseline in context. That is a per-entity statistical problem multiplied by a hundred thousand entities, refreshed continuously. No human team maintains that. It has to be computed.

Walk into any enterprise SOC at 9 a.m. and look at the queue. There will be somewhere between four hundred and four thousand alerts that arrived overnight, and a rota of tier-1 analysts expected to clear them before the next wave lands. The mathematics has not worked for years. A mid-size enterprise generates tens of millions of security events a day; even after correlation and suppression, the alert volume that survives is an order of magnitude beyond what a human team can investigate properly. So teams do what any rational actor does under impossible load: they sample. They work the highs, skim the mediums, and let the lows age out.

The uncomfortable truth is that attackers know this. Post-incident reviews keep finding the same pattern — the intrusion was detected, an alert did fire, and it sat unworked in the queue for eleven days because it was a medium riding behind three hundred other mediums. The detection stack did its job. The operating model around it failed. Detection coverage without triage capacity is not coverage at all; it is a compliance artefact that produces evidence of negligence after the breach.

Enterprises have tried to buy their way out with headcount, offshore tiers, and suppression rules. Each fix decays. Headcount churns, offshore tiers escalate anything ambiguous, and every suppression rule is a small bet that the suppressed pattern will never matter. The queue always wins — unless something other than a human works it.

The average tenure of a tier-1 SOC analyst in a large enterprise is under two years, and exit interviews are monotonous in their consistency: the work is repetitive, the false-positive rate is demoralising, and the path to interesting work is blocked by the queue itself. Organisations respond with wellness programmes and retention bonuses, treating an architectural problem as an HR problem. No bonus makes it fulfilling to close the same impossible-travel false positive forty times a week.

The cost compounds quietly. Every departure takes environment-specific knowledge that never made it into a runbook — which service accounts behave strangely at month-end, which subnet the scanner lives on, which VP travels constantly and trips geo-velocity rules. The replacement starts from zero, triages worse for six months, escalates more noise to tier 2, and the seniors who should be hunting spend their days re-answering questions the departed analyst had already answered.

This is the hidden tax of the human-queue model: the institution never accumulates judgment. Knowledge lives in people, people leave, and the SOC's effective experience level stays permanently junior no matter how long the SOC has existed. Any serious fix has to move that judgment into a system that does not resign.

SIEM economics are the quiet scandal of enterprise security budgets. Per-gigabyte pricing means the bill scales with data growth, not with value delivered — and security telemetry grows 30 to 40 percent a year as estates move to cloud. Security leaders now spend real engineering time deciding which logs not to collect, which is a sentence that should alarm every board member: the team charged with seeing everything is financially incentivised to look at less.

The workarounds have become an industry of their own. Routing verbose sources to cold storage, sampling flow logs, dropping DNS queries, keeping ninety days when investigations routinely need a hundred and eighty. Every one of these decisions is invisible until an incident, at which point the responder discovers the exact telemetry needed to scope the breach was filtered out to save four thousand dollars a month. The fine for the breach starts at seven figures.

Meanwhile the SIEM itself does less than buyers assume. It stores, indexes, and alerts. The expensive part — deciding what an alert means — is left to the customer. Enterprises effectively pay a premium tax on raw material and then pay again, in analyst salaries, to refine it.